Accuracy of pattern detection methods in the performance of golf putting

Abstract

The authors present a comparison of the classification accuracy of 5 pattern detection methods in the performance of golf putting. The detection of the position of the golf club was performed using a computer vision technique followed by the estimation algorithm Darwinian particle swarm optimization to obtain a kinematical model of each trial. The estimated parameters of the models were subsequently used as sample of five classification algorithms: (a) linear discriminant analysis, (b) quadratic discriminant analysis, (c) naive Bayes with normal distribution, (d) naive Bayes with kernel smoothing density estimate, and (e) least squares support vector machines. Beyond testing the performance of each classification method, it was also possible to identify a putting signature that characterized each golf player. It may be concluded that these methods can be applied to the study of coordination and motor control on the putting performance, allowing for the analysis of the intra- and interpersonal variability of motor behavior in performance contexts.